IEEE Transactions on Radar Systems最新文献

{"title":"Corruption Robustness Analysis of Radar Micro-Doppler Classification for Human Activity Recognition","authors":"Yi Zhou;Xuliang Yu;Miguel López-Benítez;Limin Yu;Yutao Yue","doi":"10.1109/TRS.2024.3398127","DOIUrl":"https://doi.org/10.1109/TRS.2024.3398127","url":null,"abstract":"Radar-based human activity recognition (HAR) is a popular area of research. Despite claims of high accuracy on self-collected datasets, the robustness of these models under data variations has been overlooked. This article focuses on corruption robustness analysis of radar micro-Doppler spectrogram classification for radar HAR. First, a taxonomy is proposed to classify corruptions into temporal, Doppler, and intensity domains, accompanied by strategies to effectively manage their severity for a balanced evaluation. Second, an analysis framework is presented to assess the robustness of corruption in radar sensing, providing insight into what factors to consider and how to evaluate using a dedicated corruption fmetric. Finally, a benchmarking study evaluates different model architectures and training methods to improve corruption robustness in two radar-based HAR tasks. The results indicate that higher capacity convolutional neural networks (CNNs) show improved classification accuracy, albeit with a risk of overfitting. In particular, adversarial training and data augmentation are identified as effective techniques to improve corruption robustness. However, corruption robustness is not a solved problem for the radar HAR task. Robustness to different types of corruption robustness could be dataset and model-dependent. In essence, our study contributes to a deeper understanding of the complex interplay between model architecture, training methods, and corruption robustness.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"504-516"},"PeriodicalIF":0.0,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141091107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IEEE Transactions on Radar Systems Publication Information","authors":"","doi":"10.1109/TRS.2023.3343512","DOIUrl":"https://doi.org/10.1109/TRS.2023.3343512","url":null,"abstract":"","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"C2-C2"},"PeriodicalIF":0.0,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10462556","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140063515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Highly Compressed PolSAR Model","authors":"John Becker;Julie Ann Jackson","doi":"10.1109/TRS.2024.3374640","DOIUrl":"https://doi.org/10.1109/TRS.2024.3374640","url":null,"abstract":"Additional scene information can be captured using fully polarimetric synthetic aperture radar (PolSAR) at the cost of significant increases in storage and data processing requirements. The typically sparse nature of PolSAR scenes makes compressed sensing (CS) techniques very attractive to help alleviate the increased processing and storage requirements. Here, the authors combine techniques for fast- and slow-time undersampling as well as the dropped-channel PolSAR CS technique to create a new highly compressed PolSAR model. Examples of both point target and real-world scenes are then used to demonstrate the model. Compression rates of up to 98.92% are observed for sufficiently sparse scenes.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"333-343"},"PeriodicalIF":0.0,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140123337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Timely Target Tracking: Distributed Updating in Cognitive Radar Networks","authors":"William W. Howard;Anthony F. Martone;R. Michael Buehrer","doi":"10.1109/TRS.2024.3373535","DOIUrl":"https://doi.org/10.1109/TRS.2024.3373535","url":null,"abstract":"Cognitive radar networks (CRNs) are capable of optimizing operating parameters in order to provide actionable information to an operator or secondary system. CRNs have been proposed to answer the need for low-cost devices tracking potentially large numbers of targets in geographically diverse regions. Networks of small-scale devices have also been shown to outperform legacy, large scale, high price, single-device installations. In this work, we consider a CRN tracking multiple targets with a goal of providing information which is both fresh and accurate to a measurement fusion center (FC). We show that under a constraint on the update rate of each radar node, the network is able to utilize Age of Information (AoI) metrics to maximize the resource utilization and minimize error per track. Since information freshness is critical to decision-making, this structure enables a CRN to provide the highest-quality information possible to a downstream system or operator. We discuss centralized and distributed approaches to solving this problem, taking into account the quality of node observations, the maneuverability of each target, and a limit on the rate at which any node may provide updates to the FC. We present a centralized AoI-inspired node selection metric, where a FC requests updates from specific nodes. We compare this against several alternative techniques. Further, we provide a distributed approach which utilizes the Age of Incorrect Information (AoII) metric, allowing each independent node to provide updates according to the targets it can observe. We provide mathematical analysis of the rate limits defined for the centralized and distributed approaches, showing that they are equivalent. We conclude with numerical simulations demonstrating that the performance of the algorithms exceeds that of alternative approaches, both in resource utilization and in tracking performance.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"318-332"},"PeriodicalIF":0.0,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140123352","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"HRSpecNET: A Deep Learning-Based High-Resolution Radar Micro-Doppler Signature Reconstruction for Improved HAR Classification","authors":"Sabyasachi Biswas;Ahmed Manavi Alam;Ali C. Gurbuz","doi":"10.1109/TRS.2024.3396172","DOIUrl":"https://doi.org/10.1109/TRS.2024.3396172","url":null,"abstract":"Micro-Doppler signatures (\u0000<inline-formula> <tex-math>$mu $ </tex-math></inline-formula>\u0000-DSs) are widely used for human activity recognition (HAR) using radar. However, traditional methods for generating \u0000<inline-formula> <tex-math>$mu $ </tex-math></inline-formula>\u0000-DS, such as the short-time Fourier transform (STFT), suffer from limitations, such as the tradeoff between time and frequency resolution, noise sensitivity, and parameter calibration. To address these limitations, we propose a novel deep learning (DL)-based approach to reconstruct high-resolution \u0000<inline-formula> <tex-math>$mu $ </tex-math></inline-formula>\u0000-DS directly from a 1-D complex time-domain signal. Our DL architecture consists of an autoencoder (AE) block to improve signal-to-noise ratio (SNR), an STFT block to learn frequency transformations to generate pseudo spectrograms, and, finally, a U-Net block to reconstruct high-resolution spectrogram images. We evaluated our proposed architecture on both synthetic and real-world data. For synthetic data, we generated 1-D complex time-domain signals with multiple time-varying frequencies and evaluated and compared the ability of our network to generate high-resolution \u0000<inline-formula> <tex-math>$mu $ </tex-math></inline-formula>\u0000-DS and perform in different SNR levels. For real-world data, a challenging radar-based American Sign Language (ASL) dataset consisting of 100 words was used to evaluate the classification performance achieved using the \u0000<inline-formula> <tex-math>$mu $ </tex-math></inline-formula>\u0000-DS generated by the proposed approach. The results showed that the proposed approach outperforms the classification accuracy of traditional STFT-based \u0000<inline-formula> <tex-math>$mu $ </tex-math></inline-formula>\u0000-DS by 3.48%. Both synthetic and experimental \u0000<inline-formula> <tex-math>$mu $ </tex-math></inline-formula>\u0000-DSs show that the proposed approach learns to reconstruct higher resolution and sparser spectrograms.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"484-497"},"PeriodicalIF":0.0,"publicationDate":"2024-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141078792","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Suppression of Mainlobe Interference in Radar Network via Joint Low-Rank and Sparse Recovery","authors":"Lei Zhang;Ying Luo;Huan Wang;Qun Zhang","doi":"10.1109/TRS.2024.3370927","DOIUrl":"https://doi.org/10.1109/TRS.2024.3370927","url":null,"abstract":"The challenge of effectively suppressing interference in radar systems, particularly the complex and unknown mainlobe interference, is a significant concern in radar signal processing. Traditional anti-jamming methods in single radar often fail to address this issue. The paper proposes a novel approach for suppressing mainlobe interference in radar networks, capitalizing on the low-rank representation of interferences and the sparse representation of echoes. Interference signals can be extracted by minimizing their rank with a regularization constraint after performing range and Doppler equalization on the received signals. Target echoes can be recovered through joint sparse reconstruction, exploiting their unique motion states across multiple observation points. To solve the underlying optimization problem, which involves the simultaneous reconstruction of low-rank and sparse matrices, we propose two algorithms based on the augmented Lagrangian method (ALM), with one algorithm focusing on precision and another emphasizing efficiency. This method leverages the robust spatial correlation of the interference signal and the sparsity of the target spatial distribution, allowing for effective interference suppression and accurate target echo recovery without prior knowledge of the interference type. Numerical experiments validate the effectiveness of this proposed approach and its superiority compared with other methods.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"303-317"},"PeriodicalIF":0.0,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140052911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Joint Transmit Waveform and Receive Filter Design for the DFRC System With Imperfect CSI","authors":"Chao Huang;Zhongrui Huang;Qingsong Zhou;Zhihui Li;Zhongping Yang;Jianyun Zhang","doi":"10.1109/TRS.2024.3368588","DOIUrl":"https://doi.org/10.1109/TRS.2024.3368588","url":null,"abstract":"Considering the imperfect communication channel state information (CSI), we investigated a robust joint design of transmit waveform and receive filter for the dual-functional radar-communication (DFRC) system using a multi-input-multi-output (MIMO) platform. In this study, we take the output signal-to-interference-plus-noise ratio (SINR) of the radar as the cost function, while formulating communication constructive interference (CI) constraints under imperfect estimation of communication CSI. We also imposed a constant modulus constraint to preserve the unimodular property of the transmitted waveform. To solve the complicated non-convex problem, we developed two efficient algorithms in both Euclidean and Riemannian spaces. The first algorithm used alternative direction methods of multipliers (ADMM) to break down the optimization problem into two solvable sub-problems, which were then solved using the majorization-minimization (MM) method and geometrical structure. The second algorithm converted the communication CI constraints into a distance penalty term, transformed the original problem into Riemannian space, and solved it efficiently using the Riemannian complex circle manifold conjugate gradient method. Finally, extensive simulation results demonstrate the effectiveness and superiority of both proposed algorithms.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"288-302"},"PeriodicalIF":0.0,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140042904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Wireless Distributed Frequency and Phase Synchronization for Mobile Platforms in Cooperative Digital Radar Networks","authors":"Russell H. Kenney;Justin G. Metcalf;Jay W. McDaniel","doi":"10.1109/TRS.2024.3369043","DOIUrl":"https://doi.org/10.1109/TRS.2024.3369043","url":null,"abstract":"To continue improving the performance of modern communications and radar remote sensing systems, the implementation of distributed radio frequency (RF) systems has become an increasingly active area of research. One major obstacle to implementing such a distributed network is achieving highly accurate synchronization of all RF electrical states – time, carrier phase, and carrier frequency – as without such synchronization, coherent operation amongst all systems in the network is impossible. Many techniques for achieving synchronization are not accurate enough for application in RF phase and frequency synchronization and thus cannot be applied in such networks. Others are hardware-based, making them difficult to apply to legacy systems. Moreover, many synchronization procedures require external references for establishing synchronization of one or more of the RF electrical states, limiting their application to scenarios where such external references are unavailable. Finally, many techniques are not tolerant of relative motion between platforms, making them less useful for systems such as distributed synthetic aperture radar (SAR) systems. In this paper, an RF synchronization procedure is proposed. Though its intended application is distributed radar sensor networks, it is applicable to any distributed network requiring RF coordination, such as distributed RF communication systems. The technique is capable of achieving synchronization of time, carrier phase, and carrier frequency, and can do so without external references or additional hardware. Moreover, the technique is scalable to large networks and is capable of compensating for relative motion-induced synchronization errors. The proposed technique is validated in simulations for a wide variety of operating conditions, and a three-sensor distributed SAR simulation is provided to demonstrate the effectiveness of the proposed technique in a mobile distributed radar scenario.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"268-287"},"PeriodicalIF":0.0,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140000546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Achieving n-Fold Increase in the Unambiguous Radar Range of a Uniform Pulse Train by Turning Off Every n’th Pulse (for n = 3, 4, 5…)","authors":"Nadav Levanon","doi":"10.1109/TRS.2024.3368188","DOIUrl":"https://doi.org/10.1109/TRS.2024.3368188","url":null,"abstract":"This correspondence addresses the radar challenge of extending the unambiguous delay in a uniform pulse train beyond the pulse repetition interval (PRI). The proposed approach involves dividing the streaming transmitted pulses into consecutive groups, each comprising \u0000<inline-formula> <tex-math>$n$ </tex-math></inline-formula>\u0000 pulses. These \u0000<inline-formula> <tex-math>$n$ </tex-math></inline-formula>\u0000 transmitted pulses undergo overlay with a coded sequence \u0000<inline-formula> <tex-math>${text{S}}_{n}$ </tex-math></inline-formula>\u0000 (e.g., S3 = {1 1 0}). Concurrently, the corresponding \u0000<inline-formula> <tex-math>$n$ </tex-math></inline-formula>\u0000 reference pulses in the receiver undergo overlay with a coded sequence \u0000<inline-formula> <tex-math>${text{R}}_{n}$ </tex-math></inline-formula>\u0000 (e.g., R3 = {\u0000<inline-formula> <tex-math>$1,,1-1$ </tex-math></inline-formula>\u0000}), requiring a sidelobe-free periodic cross-correlation between \u0000<inline-formula> <tex-math>${text{S}}_{n}$ </tex-math></inline-formula>\u0000 and \u0000<inline-formula> <tex-math>${text{R}}_{n}$ </tex-math></inline-formula>\u0000. The initially identical transmitted pulses may be either plain or compressed, and the corresponding reference pulses can be matched or mismatched. This innovative approach extends the unambiguous range by a factor of \u0000<inline-formula> <tex-math>$n$ </tex-math></inline-formula>\u0000. However, it does not address the issue of masked target returns coinciding with detection of the system’s own pulses, when the isolation of own pulses is insufficient and they saturate the receiver. Notably, the proposed approach is applicable to both coherent and non-coherent systems such as Lidar. However, our emphasis here is mainly on non-coherent systems. The presentation includes simple examples with \u0000<inline-formula> <tex-math>$n$ </tex-math></inline-formula>\u0000 values of 3, 4, and 5, and considers system performances in the presence of noise.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"263-267"},"PeriodicalIF":0.0,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140000579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sidelobes and Ghost Targets Mitigation Technique for High-Resolution Forward-Looking MIMO-SAR","authors":"Adnan Albaba;Marc Bauduin;Adham Sakhnini;Hichem Sahli;André Bourdoux","doi":"10.1109/TRS.2024.3366779","DOIUrl":"https://doi.org/10.1109/TRS.2024.3366779","url":null,"abstract":"We propose and analyze an image reconstruction algorithm for high angular resolution with a forward-looking multiple-input multiple-output synthetic aperture radar (FL-MIMO-SAR). This algorithm achieves significant attenuation levels of the sidelobes (SL) and ghosts such as grating lobes (GL) and Doppler left-right ambiguity (DLRA). Aspects such as SAR image reconstruction, high SLs in SAR images, the formation of GLs, DLRA, and the overlapping between DLRA ghosts and nearby GLs are investigated. In addition, different imaging scenarios are simulated using a frequency-modulated continuous wave (FMCW) radar simulator and validated with real measurements using different time-division multiplexing MIMO FMCW radars. The simulation and experimental results show that the proposed algorithm manages to significantly enhance the SAR image by attenuating the SLs and canceling ghosts and ambiguities. Furthermore, the proposed algorithm results in true targets with narrower response patterns, which improves the detectability of targets in the SAR image.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"237-250"},"PeriodicalIF":0.0,"publicationDate":"2024-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139937153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}